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Applying Latent Variable Modeling to Cormorbidity Treatment Research

将潜变量模型应用于疾病治疗研究

基本信息

批准号：
8528426
负责人：
MATT G KUSHNER
金额：
$ 10.86万
依托单位：
UNIVERSITY OF MINNESOTA
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-09-25 至 2014-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8528426
关键词：
Accounting Address Affect Affective Alcohol dependence Alcohols Anxiety Anxiety Disorders Applications Grants Beryllium Categories Clinical Communities Data Data Set Dependence Development Plans Diagnosis Disease Educational process of instructing Equation Expectancy Factor Analysis Family Fright Generalized Anxiety Disorder Generations Goals Growth Head Individual Intervention Knowledge Label Learning Link Manuscripts Measurement Measures Mediating Mediator of activation protein Mentors Meta-Analysis Methodology Modality Modeling Modification Moods Morbidity - disease rate Outcome Parents Patients Population Principal Investigator Production Psychiatric therapeutic procedure Psychosocial Factor Randomized Recording of previous events Relapse Reporting Research Research Support Risk Sampling Services Severities Shapes Social Phobia Staging Structural Models Structure Students Symptoms Techniques Testing Time Training Translating Treatment Protocols Treatment outcome Work addiction alcohol abuse therapy alcohol relapse alcohol research alcohol use disorder alcoholism therapy base career career development drinking effective therapy expectation experience falls follow-up improved interest problem drinker programs psychosocial response skills theories trait treatment planning treatment program use alcohol to cope

项目摘要

DESCRIPTION (provided by applicant): The objective of this K02 application is to allow the candidate to increase the time he has allocated to research and related career development activities from a projected annual average of between 25-45% without the K02 to between 75-80% with the K02. The application overviews the candidate's 15-year history of NI/\AA-supported alcohol research and provides a career development plan aimed at acquiring and deploying sophisticated data analytic and methodological strategies that fall under the broad heading of latent variable-structural equation modeling (LV-SEM) (e.g., factor analysis, path analysis, latent class and trait modeling, growth mixture modeling, item response theory). This would be accomplished via specific career development objectives involving: 1) interactions with mentors and collaborators (15%); 2) hypotheses testing (25%); 3) formal courses/training (25%); 4) scholarly production (25%); and, 5) teaching/ service (10%). The "parent R01" ("CBT Treatment of Anxiety Disorders in Comorbid Alcoholics" /\A0105069) targets a sample of 400 patients undergoing a standard community-based alcoholism treatment program who are also diagnosed with at least one of several common comorbid anxiety and affective ("internalizing") disorders. In addition to the alcohol treatment, patients receive one of two psychosocial treatments for internalizing disorder. LV-SEM would allow the candidate to empirically partition highly inter- correlated internalizing disorders/symptoms in the parent R01 dataset into distinct vs. common components that could then be related in causal models to the alcohol and internalizing treatment outcomes. Next, the candidate would replicate these models in community, student and psychiatric-based datasets that are either publicly available (e.g., NESARC) or available via his collaborators (e.g., Drs. Ken Sher and Carrie Randall). These efforts would provide an empirically parsimonious characterization of the internalizing problems experienced by individuals with alcohol use disorders and how these relate to alcoholism and psychiatric treatments across a number of populations and treatment modalities. The longer-term goal would be to develop more effective treatments for alcohol disorders with comorbid internalizing disorders. RELEVANCE: By further clarifying the best clinical strategies for and conceptualization of internalizing disorders occurring in alcoholism treatment patients, the proposed work will improve our ability to effectively treat alcohol dependence.

描述（由申请人提供）：此K 02应用程序的目的是允许候选人增加他分配给研究和相关职业发展活动的时间，从没有K 02的预计年平均25-45%到K 02的75-80%之间。该应用程序概述了候选人15年的NI/\AA支持的酒精研究历史，并提供了一个职业发展计划，旨在获取和部署复杂的数据分析和方法策略，这些策略属于潜在变量结构方程建模（LV-SEM）的大标题（例如，因子分析、路径分析、潜在类别和特质建模、生长混合建模、项目反应理论）。这将通过具体的职业发展目标来实现，包括：1）与导师和合作者的互动（15%）; 2）假设测试（25%）; 3）正式课程/培训（25%）; 4）学术生产（25%）;以及，5）教学/服务（10%）。“父R 01”（“CBT治疗共病酒精中毒者的焦虑障碍”/\A0105069）针对400名接受标准社区酒精中毒治疗计划的患者样本，这些患者也被诊断患有几种常见的共病焦虑和情感（“内化”）障碍中的至少一种。除了酒精治疗外，患者还接受两种内化障碍的心理社会治疗之一。LV-SEM将允许候选者凭经验将父R 01数据集中高度相互关联的内化障碍/症状划分为不同的与共同的组分，然后可以在因果模型中将其与酒精和内化治疗结果相关。接下来，候选人将在社区、学生和基于精神病学的数据集中复制这些模型，这些数据集可以是公开的（例如，NESARC）或可通过其合作者获得（例如，Ken Sher和Carrie Randall博士）。这些努力将提供一个经验性的简约表征的内化问题所经历的个人与酒精使用障碍，以及如何将这些与酗酒和精神病治疗在一些人群和治疗方式。长期目标是开发更有效的治疗酒精障碍与共病内化障碍。相关性：通过进一步阐明最佳的临床策略和概念化的内化障碍发生在酒精中毒治疗患者，拟议的工作将提高我们的能力，有效地治疗酒精依赖。